Most findings about molecular interactions and cellular regulatory events are published in peer-reviewed scientific literature in the form of scientific jargon. The computerized text-mining algorithms are used to convert free grammar of human language into a set of formalized relationships between biological concepts in order to use this wealth of information. The compendium of such interactions extracted from an entire set of biomedical literature is a called knowledge network. Knowledge networks are the first step in the process of digitizing molecular biological knowledge. The next step is building molecular models depicting principal molecular events that govern various biological processes. Data mining in knowledge networks is the essence of building new biological models. The purpose is to elucidate major pathways of information flow through a molecular physical interaction network that happens during a disease, a cell process or an experiment. Such models contain key proteins involved in the process and can be used for prioritizing disease targets, for understanding of drug action and prevention of drug-induced toxicities, for analysis of patient predispositions and design of personalized therapies, for design of diagnostic biomarkers and analysis of patient molecular data. This e-book contains detailed examples illustrating the path to the digital biology and computerized drug development for personalized medicine. It provides conceptual principals for building biological models and for applying the models to make predictions relevant for drug development and translational medicine.
The e-book will also be useful for researchers who use high-throughput technologies for molecular profiling of disease and drug action. It provides examples for analysis of gene expression microarrays to infer biological models, to find biomarkers for drug response and for applications of high-throughput molecular profiling technologies for personalized medicine. Scientists in academia, in pharmaceutical industry as well as graduate students will benefit from reading this book. The illustrations from the book can also be readily used in taught courses for molecular biology and pharmacology.

Abstract

Patient stratification or, a personalized approach to medical treatment, is a
promising approach in modern medicine. Finding biological patterns within a group of
patients with the same diagnosis could lead to more precise and effective therapies. To
address this issue it is necessary to reveal different mechanisms within the same disease,
to find new biomarkers, and to develop new diagnostic tests that would distinguish
patients from different subgroups.

Cancer sub-typing based on clustering of individual patient gene expression profiles has
been widely used for various types of cancer. Here we propose a new approach which
includes the consecutive use of Sub-network enrichment analysis algorithm (SNEA) for
individual differential expression profiles and biclustering of found expression
regulators and samples.

We analyzed nine publicly available microarray datasets with data from patients
suffering from colorectal cancer as compared to healthy donors, including one dataset
containing supplementary information on patient response to anti-EGFR therapy with
cetuximab. We have identified several patient subtypes characterized by specific
regulatory clusters (pathways) and mapped the data about cetuximab response onto the
heat map of pathway activity for each patient. We found that the most prominent
mechanism that distinguished responders from non-responders is dependent on
regulators from the TGF-β/SMAD pathway and corresponds to the epithelial-tomesenchymal
transition (EMT).